


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Tuple      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					List[Any]      =       {
    "tanreinama/GPTSAN-2.8B-spout_is_uniform": (
        "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
    ),
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'gptsan-japanese'
    __magic_name__         =						[
        'past_key_values',
    ]
    __magic_name__         =						{
        'hidden_size': 'd_model',
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }
    def __init__(			self     ,   __snake_case=3_6_0_0_0     ,   __snake_case=1_2_8_0     ,   __snake_case=1_0_2_4     ,   __snake_case=8_1_9_2     ,   __snake_case=4_0_9_6     ,   __snake_case=1_2_8     ,   __snake_case=1_0     ,   __snake_case=0     ,   __snake_case=1_6     ,   __snake_case=1_6     ,   __snake_case=1_2_8     ,   __snake_case=0.0     ,   __snake_case=1E-5     ,   __snake_case=False     ,   __snake_case=0.0     ,   __snake_case="float32"     ,   __snake_case=False     ,   __snake_case=False     ,   __snake_case=False     ,   __snake_case=0.002     ,   __snake_case=False     ,   __snake_case=True     ,   __snake_case=3_5_9_9_8     ,   __snake_case=3_5_9_9_5     ,   __snake_case=3_5_9_9_9     ,   **__snake_case     ,   ):
         snake_case											=							vocab_size
         snake_case											=							max_position_embeddings
         snake_case											=							d_model
         snake_case											=							d_ff
         snake_case											=							d_ext
         snake_case											=							d_spout
         snake_case											=							num_switch_layers
         snake_case											=							num_ext_layers
         snake_case											=							num_switch_layers + num_ext_layers
         snake_case											=							num_heads
         snake_case											=							num_experts
         snake_case											=							expert_capacity
         snake_case											=							dropout_rate
         snake_case											=							layer_norm_epsilon
         snake_case											=							router_bias
         snake_case											=							router_jitter_noise
         snake_case											=							router_dtype
         snake_case											=							router_ignore_padding_tokens
         snake_case											=							output_hidden_states
         snake_case											=							output_attentions
         snake_case											=							initializer_factor
         snake_case											=							output_router_logits
         snake_case											=							use_cache

         super().__init__(
             separator_token_id=__snake_case     ,   pad_token_id=__snake_case     ,   eos_token_id=__snake_case     ,   **__snake_case     ,   )








def      UpperCAmelCase__		(UpperCamelCase_ = 10_00							):



     """simple docstring"""






     return sum(2 * a * ((a - 1) // 2) for a in range(3				,n + 1							)							)


if __name__ == "__main__":
 print(solution())




